Identification of Potential Biomarkers for Psoriasis by DNA Methylation and Gene Expression Datasets

Abstract

DNA methylation (DNAm) plays an important role in the pathogenesis of psoriasis through regulating mRNA expressions. This study aimed to identify hub genes regulated by DNAm as biomarkers of psoriasis. Psoriatic skin tissues gene expression and methylation datasets were downloaded from Gene Expression Omnibus (GEO) database. Subsequently, multiple computational approaches, including immune infiltration analysis, enrichment analysis, protein–protein interaction (PPI) network establishment, and machine learning algorithm analysis (lasso, random forest, and SVM-RFE), were performed to analyze the regulatory networks, to recognize hub genes, and to clarify the pathogenesis of psoriasis. Finally, the hypermethylated genes were used to immune cell infiltration analysis, which revealed that psoriasis skin tissues were mainly composed of activated dendritic cells, resting mast cells, T follicular helper cells (cTfh), etc. Differentially expressed-methylated genes (DEMGs) were identified and partitioned into four subgroups and the 97 significantly hypermethylated and downregulated (hyper-down) genes accounted for the highest proportion (47%). Hyper-down genes were mainly enriched in glucose homeostasis, AMP-activated protein kinase (AMPK) signaling pathway, lipid storage disease, partial lipodystrophy, and insulin resistance. Furthermore, insulin receptor substrate 1 (IRS1), Rho guanine nucleotide exchange factor 10 (ARHGEF10) and retinoic acid induced 14 (RAI14) were identified as potential targets. These findings provided new ideas for future studies of psoriasis on the occurrence and the molecular mechanisms.

Mast cells as important regulators in the development of psoriasis

“…In addition, a decrease in the total number of MCs and an increase in the proportion of activated MCs in psoriatic lesions have been reported (26). However, Liu et al used the CIBERSORT method to evaluate immune cell infiltration based on hypermethylated genes in psoriasis and found a significant increase in resting MCs (27). The difference in results between studies may be related to the different stages of psoriatic lesions in the datasets.…”

“…However, Liu et al. used the CIBERSORT method to evaluate immune cell infiltration based on hypermethylated genes in psoriasis and found a significant increase in resting MCs ( 27 ). The difference in results between studies may be related to the different stages of psoriatic lesions in the datasets.…”

Identification and Experimental Validation of Marker Genes between Diabetes and Alzheimer’s Disease

“…The least absolute shrinkage and selection operator (LASSO) logistic regression [ 29 ] with the “glmnet” package (version 4.1-1) and the support vector machine-recursive feature elimination (SVM-RFE) [ 30 ] with the “e1071” package (version 1.7-6) were applied to screen the specific genes. The obtained results of the two algorithms were intersected and displayed in a Venn diagram, and all of them were further screened through the combination of Comparative Toxicogenomics Database (CTD; http://ctdbase.org/ ) [ 31 ] and the GSE5281 dataset.…”

Identification of Potential Biomarkers and Small Molecule Drugs for Cutaneous Melanoma Using Integrated Bioinformatic Analysis

“…The expression microarray datasets were all standardized by quantiles. A linear model was used to assess differential expression between CM cases and normal skin controls using R package named “limma.” The |log2 fold change (FC)| > 1 and p -value < 0.05 were regarded as the cut-off criteria to determine DEGs ( Liu et al, 2021 ).…”